Pulse oximeters determine an oxygen saturation level of a patient's blood, or related analyte values, based on transmission/absorption characteristics of light transmitted through or reflected from a patient's tissue. In particular, pulse oximeters generally include a probe for attaching to a patient's appendage such as a finger, earlobe or nasal septum, or another location, particularly in the case of reflective oximeters. The probe is used to transmit pulsed optical signals of at least two wavelengths, typically red and infrared, to the patient's tissue. The transmitted signals are received by a detector that provides an analog electrical output signal representative of the received optical signals. By processing the electrical signal and analyzing signal values for each of the wavelengths at different portions of the patient's pulse cycle, information can be obtained regarding blood oxygen saturation and/or other parameter values such as pulse rate, or blood pressure/blood volume related values.
The algorithms for determining blood oxygen saturation related values are normally implemented in a digital processing unit. Accordingly, one or more analog-to-digital (A/D) converters are generally interposed between the detector and the digital processing unit. Additionally, the detector signal is generally demodulated and demultiplexed by signal processing components. Demodulation involves separating the physiological signal of interest (generally including a more rapidly changing AC portion including a plethysmographic waveform and an optically based “DC” offset due to slowly changing absorption values associated with non-pulsatile tissue absorption) from a carrier waveform associated with the flashing optical sources. Demultiplexing involves separating the different wavelength components associated with the different signal sources. That is, because blood oxygen saturation is calculated based on differential absorption values for different transmitted optical signal wavelengths, the detected signal is generally separated, or demultiplexed, into at least two different wavelength components. Typically, demodulation and demultiplexing have been implemented in analog circuitry operatively disposed between the optical signal detector and the analog-to-digital converter(s), but can be digitally implemented.
A persistent problem in the field of pulse oximetry is eliminating or otherwise accounting for noise and other artifact that can easily obscure or interfere with the pulsatile signals of interest. Some of the sources of this artifact include power line noise, electrical noise from other medical equipment, and artifact associated with patient motion. In this regard, certain filtering techniques have been employed both on the front end (i.e., in the analog circuitry between the optical signal detector and the analog-to-digital converter or converters) and in the back end (i.e., in the digital domain based on the signal from the analog-to-digital converter or converters) of the signal processing components.
Such front end filtering is generally used to filter the modulation signal as opposed to the physiological signal of interest. In this regard, the modulation signal may be approximately in the form of a square wave whereas the physiological signal of interest, which is carried by the modulation signal, may be in the form of a plethysmographic waveform. The front end filtering may include high pass and low pass filtering. For example, a low pass filter may be used to reject certain high frequency electronic noise and a high pass filter may be used to exclude certain low frequency phenomenon. Thus, such front end filtering is generally used to pass a broad frequency range including the modulation frequency or frequencies and is not directed to targeted elimination of interference with respect to the AC portion of the signal.
Back end filtering is sometimes used to filter noise from the physiological signal of interest. This often involves frequency dependent filtering such as bandpass filtering. Unfortunately, some sources of artifact can include frequency components within the physiological range of interest. For example, motion artifact may be observed within the physiological range of interest. With regard to motion, a number of different digital filtering or other compensation algorithms have been proposed or implemented with varying degrees of success. However, in some cases, these algorithms may either fail to satisfactorily address the effects of motion artifact or may filter out useful pulsatile information to an undesirable extent.
Other approaches to addressing artifact involve deemphasizing or excluding parameter calculations deemed to be based on data that is significantly affected by motion or other artifact. For example, presumed high artifact conditions have been identified based on an analysis of a spectrum of the detector signal to identify spectral characteristics indicative of artifact or the absence of spectral characteristics indicative of a well-defined pulsatile signal. In other cases, presumed high artifact conditions have been identified based on a result of calculations deemed unlikely to have a physiological basis, e.g., calculated values corresponding to an unlikely value of arterial oxygen saturation, an abrupt change thereof, or an unlikely variance from a trend in data related to oxygen saturation with respect to a time window under consideration.
Upon identification of such artifact conditions, associated calculated values may be ignored for purposes of determining a result or may be deweighted, for example, by increasing the size of a time window of data used for calculations (thereby presumably reducing the impact the motion affected data on the result) or by applying confidence or weighting factors to each of a series of calculated values used in obtaining a resulting value, so as to achieve a kind of weighted average wherein motion affected data is deemphasized. However, such approaches have had limited success in addressing a variety of motion conditions. Moreover, in some cases, such approaches have required difficult or questionable judgments in distinguishing different motion conditions, have required complicated processing and/or have limited the methodologies available for physiological parameter calculations.